Search

For 1–3 piezoelectric composite high-power transducers applied to FUS surgery, avoiding the electro-acoustic output performance fluctuating with the temperature is an important task. In this work, 1–3 type piezocomposites were fabricated with dice and fill method. The PZT4 piezoceramic was used as active phase, and mica powder modified epoxy resin E51 was used as passive phase: the mass ratio of the epoxy and mica powder was 10:x. Those composites were evaluated for power transducer applications with air backing and without front layer. The effect of mica powder modified polymer filler on the electro-acoustic output behavior and thermal stability of the transducer were detailed investigated. The results show that mica power could increase the storage modulus and the toughness of the polymer. As a result, an elevated kt of 0.7 was observed when the adding amount was 50 wt%. The electro-acoustic conversion efficiency (ηea) of the transducer substantial increased to 81–83% and with good thermal stability when x = 1–5. The research results show that adding mica powder in the filler materials was one of the potential methods to prepare high-power piezocomposite transducer.

KNN based lead free piezo-ceramics with new additive (Bi0.45La0.05K0.5)ZrO3 (BLKZ) doping have been prepared by conventional solid-state sintering method. The effect of BLKZ content on phase structure evolution was investigated through the X-ray diffraction and the Rietveld refinement method. Both the XRD and Rietveld refinement confirmed a coexistence of rhombohedral–orthorhombic–tetragonal (R–O–T) multiphase in the BLKZ adding KNN systems. The study of the BLKZ content on the phase transition temperature of the ceramics manifested that the BLKZ could modify the phase transition temperature of rhombohedral–orthorhombic (T R–O) and orthorhombic–tetragonal (T O–T) to room temperature simultaneously, thus, a new R–O–T multiphase coexistence boundary would be constructed in the KNN systems. A piezoelectric coefficient of d 33 ~ 385 pC/N with a T c of 245 °C were observed in the KNN ceramics when the BLKZ content is 3.5 mol%.

In this work, lead free piezo-ceramics of (1−x)K0.4Na0.6Nb0.96Sb0.04O3–xBi0.5K0.5Zr0.9Hf0.1O3 [(1−x)KNNS–xBKZH] have been synthesized by conventional solid-state sintering method. The effect of BKZH content on the phase structure and electrical properties were systematically investigated. The results show that a coexistence of rhombohedral–orthorhombic–tetragonal (R–O–T) phases have been found when x = 0.04, indicating that BKZH doping can induce the formation of R–O–T phase boundary by increasing T R−O and decreasing T O−T simultaneously in KNN systems. In addition, an optimal electrical properties (e.g., d 33 = 451 pC/N, k p = 52%, T c = 258 °C, e r = 2489, tanδ = 3.6%, P r = 21.7 μC/cm2, and E c = 10.1 kV/cm) were obtained in the ceramic of x = 0.04, which proves that adding BKZH content is an efficientive way to promote the electrical properties of potassium–sodium niobate ceramics. As a result, we can believe that the (1−x)KNNS–xBKZH ceramics will become one of the promising lead-free candidates for piezoelectric applications.

Ensemble learning is a wildly concerned issue. Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers. They fail to address the ensemble task where only unlabeled data are available. A label propagation based ensemble (LPBE) approach is proposed to further combine base classification results with unlabeled data. First, a graph is constructed by taking unlabeled data as vertexes, and the weights in the graph are calculated by correntropy function. Average prediction results are gained from base classifiers, and then propagated under a regularization framework and adaptively enhanced over the graph. The proposed approach is further enriched when small labeled data are available. The proposed algorithms are evaluated on several UCI benchmark data sets. Results of simulations show that the proposed algorithms achieve satisfactory performance compared with existing ensemble methods.